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On the Configuration of More and Less Expressive Logic Programs

Published online by Cambridge University Press:  21 March 2022

CARMINE DODARO Open the ORCID record for CARMINE DODARO [Opens in a new window] ,
MARCO MARATEA Open the ORCID record for MARCO MARATEA [Opens in a new window]  and
MAURO VALLATI
CARMINE DODARO
Affiliation:
University of Calabria, Rende, Italy (e-mail: dodaro@mat.unical.it)
MARCO MARATEA
Affiliation:
University of Genoa, Genoa, Italy (e-mail: marco.maratea@unige.it)
MAURO VALLATI
Affiliation:
University of Huddersfield, Huddersfield HD1 3DH, UK (e-mail: m.vallati@hud.ac.uk)
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Abstract

The decoupling between the representation of a certain problem, that is, its knowledge model, and the reasoning side is one of main strong points of model-based artificial intelligence (AI). This allows, for example, to focus on improving the reasoning side by having advantages on the whole solving process. Further, it is also well known that many solvers are very sensitive to even syntactic changes in the input. In this paper, we focus on improving the reasoning side by taking advantages of such sensitivity. We consider two well-known model-based AI methodologies, SAT and ASP, define a number of syntactic features that may characterise their inputs, and use automated configuration tools to reformulate the input formula or program. Results of a wide experimental analysis involving SAT and ASP domains, taken from respective competitions, show the different advantages that can be obtained by using input reformulation and configuration.

Keywords

SATisfiabilityanswer set programmingknowledge configuration
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 23 , Issue 2 , March 2023 , pp. 415 - 443
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Copyright
© The Author(s), 2022. Published by Cambridge University Press

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Footnotes

*

Mauro Vallati was supported by a UKRI Future Leaders Fellowship [grant number MR/T041196/1].

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